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state-of-industry report that loosely follows a Triple Take macro arc (Facts->Implications->Action); tactically it leans on Pattern Hunter loops, callback/payoff to last year's predictions, and concrete-language action titles, but is not a tight Storymakers deck — it reads as a curated evidence anthology more than a single argument.", "imagesInspected": 6, "extractionSeconds": 473.62338 }, "score": { "backend": "codex", "scoredAt": "2026-05-02 13:24:47.271+00", "subScores": { "scqa_arc": 42, "action_titles": 80, "mece_structure": 67, "closing_strength": 58, "evidence_quality": 86, "clarity_of_thesis": 46, "production_quality": 72, "visual_storytelling": 78 }, "totalScore": 66, "coveragePct": 100, "explanations": { "scqa_arc": "The deck gives situation and context through the agenda, executive summary, and prediction scorecard (slides 3, 7, 9), but it lacks a crisp complication-question-answer spine and reads mainly as a trend-by-trend analytical inventory.", "action_titles": "Action-title density is high at 165 of 188 slides, with strong examples such as \"Self-supervision is taking over computer vision\" on slide 12, but front matter and section labels dilute consistency.", "mece_structure": "The Research, Talent, Industry, Politics, and Predictions architecture is broadly orderly (slides 10, 75, 92, 153, 182), but industry, governance, politics, investing, and safety topics overlap and leave the report feeling curated rather than exhaustive.", "closing_strength": "The ending provides eight forward-looking predictions on slide 183 and a feedback ask on slide 185, but it does not land a decisive recommendation, owner, or action plan.", "evidence_quality": "Most substantive claims are backed by metrics, benchmark tables, maps, case studies, or quotes, for example slide 174 quantifies 20 of 42 agencies and slide 163 benchmarks GPT-Neo against GPT-3.", "clarity_of_thesis": "The first five slides establish identity, authors, agenda, acknowledgments, and 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divider as a pillar claim, for example a one-sentence conclusion for Research, Talent, Industry, and Politics rather than only the section name.", "Add synthesis slides at the end of each major pillar that answer: what changed, why it matters, and what it implies for the next section.", "Replace the current close with a recommendation page after slide 183 that translates the predictions into actions for investors, operators, researchers, and policymakers.", "Tighten long action titles into one claim plus one proof point, and avoid generic prefixes such as \"Machine Learning in production:\" when the title can state the insight directly." ], "closingScore": 56, "openingScore": 42, "topStrengths": [ "The macro architecture is easy to navigate: slide 7 summarizes the four main pillars, then slides 10, 75, 92, and 153 segment the analysis cleanly before predictions on slide 183.", "Many titles carry the insight and the evidence, especially quantified ones like slide 105, \"Viz.ai's stroke 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Users can avoid this choice altogether via prompt learning, where prompts are formulated as learnable vectors.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:44:41.634+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "edfb5f6f-6c40-4f49-87f0-d082636c4896", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.4, "x": 0.55, "y": 0.85 }, "kind": "source-note", "text": "Figure 1. Average scores on 7 dev datasets of SuperGlue. 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"name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-25 12:04:04+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-47c3-7407-8d2c-ca9279bd9ae6", "parentComponentId": null }, { "bbox": { "h": 0.35, "w": 0.45, "x": 0.02, "y": 0.38 }, "kind": "list", "text": "In 2020, 36 of the 42 “Double First Class” universities were ranked in the top 500 universities globally, and 21 were ranked in the top 200.\nMost of the recent growth in the number of PhDs comes from elite universities administered by the Ministry of Education: these universities accounted for 65% of the 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"canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-25 12:04:04+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-47c3-7407-8d2c-cd91aa6f3aab", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.45, "x": 0.02, "y": 0.2 }, "kind": "paragraph", "text": "High count numbers can artificially hide a decrease in program quality, for example if driven by a rapid 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Data shows that this is not the case in China, where 43% of PhD graduates in 2019 came from Double First Class Universities, a slight drop from 46% in 2015.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:44:47.77+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b1c1f2fc-6201-44a6-b786-728d8c4fcf05", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.4, "x": 0.5, "y": 0.8 }, "kind": "source-note", "text": "Source: Chinese Ministry of Education, Double First Class University Employment Quality Reports.", "attrs": null, "canon": { "kind": { "name": "Source note", "slug": "source-note", "status": "active", "canonId": "23484952-494b-4a5e-b1ad-550d0d70e948", "version": 1, "description": "Attribution / source / caveat placed at the slide footer. 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Number of PhDs awarded in China by university category, 2010-2019", "attrs": null, "canon": { "kind": { "name": "Table", "slug": "table", "status": "active", "canonId": "019de52c-f9c8-7148-8119-eaac9594cd28", "version": 1, "description": "Tabular data layout." }, "tool": null, "subkind": { "name": "Data table", "slug": "data", "status": "active", "canonId": "019de52d-02d5-75b8-8abf-88e92bf63569", "version": 1, "description": null }, "framework": null }, "subkind": "data", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:44:47.77+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e21e4fd2-8be1-4281-8468-d3619e1d4563", "parentComponentId": null }, { "bbox": { "h": 0.04, "w": 0.4, "x": 0.02, "y": 0.14 }, "kind": "title", "text": "...without sacrificing program quality", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 17:44:47.77+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ec7a50c7-6a6c-46e4-b2fa-7bd7cc5c7382", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Anadiplosis", "slug": "anadiplosis", "status": "active", "bestFor": null, "canonId": "019dd956-a0a5-75f9-a98c-7b7d8ee377a1", "version": 1, "bodyDocId": null, "description": "Ending one clause with a word that begins the next", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Storyteller", "layer": "loop", "agents": [ "Storyteller" ], "matchId": "019dd95a-0fd5-7148-8ed1-5e6ebebc2b45", "evidence": "Ellipsis links pp.78 and 79 titles into one sentence.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 65, "extraction": { "at": "2026-04-29 13:07:31.544884+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 79, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. 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The exception (Sparkline) ends with new bliss which is functionally a resolution." }, "alignedBlockIds": null, "matchConfidence": 55 } ], "loops": [], "locked": true } ], "arcBeats": [ { "from": 5, "to": 152, "label": "The Facts (What)", "description": "Definitions p.5-6, Exec Summary p.7, then Sections 1-3 catalog evidence." }, { "from": 153, "to": 181, "label": "The Implications (So What)", "description": "Section 4 Politics: AI Safety, Governance, Regulation, Military implications." }, { "from": 182, "to": 188, "label": "The Action (Now What)", "description": "Section 5 '8 predictions for the next 12 months' + closing." } ], "loops": [ { "from": 8, "to": 9, "label": "Pattern Hunter", "description": "Score the team's 2020 predictions" }, { "from": 11, "to": 18, "label": "Pattern Hunter", "description": "Show transformers spreading across AI domains" }, { "from": 19, "to": 27, "label": "Pattern Hunter", "description": "Demonstrate AI's expansion into structural biology and drug discovery" }, { "from": 28, "to": 32, "label": "Pattern Hunter", "description": "Survey reinforcement learning advances + concerns" }, { "from": 36, "to": 40, "label": "Aha Moment", "description": "Reveal multimodal generative AI breakthrough" }, { "from": 41, "to": 51, "label": "Pattern Hunter", "description": "Trace LLM frontier and the prompting paradigm" }, { "from": 52, "to": 55, "label": "Iceberg", "description": "Surface bias problems in medical AI" }, { "from": 56, "to": 63, "label": "Iceberg", "description": "Diagnose openness and trust crisis in ML research" }, { "from": 64, "to": 69, "label": "Pattern Hunter", "description": "Show GNNs becoming mainstream" }, { "from": 76, "to": 80, "label": "Pattern Hunter", "description": "Establish China's rise in AI talent and research" }, { "from": 81, "to": 88, "label": "Cost Of Inaction", "description": "Argue academic brain drain is unsustainable" }, { "from": 93, "to": 97, "label": "Pattern Hunter", "description": "Show AI delivering in drug discovery" }, { "from": 98, "to": 105, "label": "Pattern Hunter", "description": "Catalogue real-world AI deployments across industries" }, { "from": 106, "to": 111, "label": "Iceberg", "description": "Diagnose ML-in-production challenges" }, { "from": 112, "to": 118, "label": "Cost Of Inaction", "description": "Frame semiconductor sovereignty crisis" }, { "from": 119, "to": 123, "label": "Pattern Hunter", "description": "Show AI companies dominating public markets" }, { "from": 141, "to": 152, "label": "Build Up", "description": "Build up the size of the AI investment opportunity" }, { "from": 154, "to": 159, "label": "Cost Of Inaction", "description": "Argue AI safety is dangerously underinvested" }, { "from": 160, "to": 165, "label": "Pattern Hunter", "description": "Survey AI governance experiments" }, { "from": 166, "to": 174, "label": "Tale Two Worlds", "description": "Contrast EU vs China vs US regulatory approaches" }, { "from": 175, "to": 181, "label": "Pattern Hunter", "description": "Document the operationalisation of military AI" }, { "from": 182, "to": 185, "label": "Time Machine", "description": "Project forward with 8 predictions" } ] }